Issue Tree Decomposition

Issue Tree Decomposition

Use when a broad initiative or recurring review needs to be turned into a structured set of future GitHub issues.

When to use

• User asks to "create future gh issues"
• There is one umbrella issue but implementation needs multiple focused tracks
• You want to split work by lanes such as operations, knowledge, automation, reporting
• You want Claude subagents to help with analysis, but repo writes must stay in the main session

Core pattern

1. Identify the umbrella issue and current related issues.
2. Add or update a single documentation page that describes the initiative and its current issue map.
3. Use delegate_task with Claude subagents for READ-ONLY gap analysis by lane.
4. Ask each subagent for:
   • 2-5 non-duplicate child issue proposals
   • title
   • rationale
   • deliverables
5. Create the selected issues locally in the main session with gh issue create.
6. Update the initiative doc with the new issue links.
7. Comment on the umbrella issue summarizing the new split.
8. Verify the issue URLs and the doc updates.

Use todo to manage each wave explicitly:

• delegate analysis
• create issues
• update doc/parent issue
• verify

This makes repeated decomposition waves much safer when you are going 3-5 layers deep.

Important constraint

Subagents are for analysis only. Do NOT rely on delegate_task subagents to write repo files or create GitHub issues. They run in isolated sandboxes and returned summaries are compressed. Use them to think; do the writes yourself.

Two practical lessons:

• Local/open-issue snapshots can lag behind newly created issues. For the latest parent/child grounding, prefer live gh issue view calls in the main session.
• If the initiative doc has been linked from README files but is unexpectedly missing in the working tree, recreate it immediately from the current issue map instead of stalling. Then re-link or re-patch the README surfaces as needed.

Recommended lane split

A good first pass is 3 parallel subagents:

• machine readiness / execution operations
• intelligence accessibility / knowledge systems
• automation / reporting / governance

For a review-blocked approval-stage issue, use this specialized 3-lane split instead:

• lane 1: tighten the canonical plan (deliverable, scope boundaries, tests, acceptance criteria)
• lane 2: design the child-issue decomposition (2-4 concrete non-overlapping follow-up issues with dependency order)
• lane 3: rewrite the parent GitHub issue body to a narrower v1 / foundation-only scope

This pattern works especially well when adversarial review returns a MAJOR because the issue is too broad. It lets you revise the parent scope and produce the follow-up issue tree in one pass instead of doing serial rewrite cycles.

A good second pass is to recurse one level deeper on the strongest tracks:

• schema/contracts
• evidence artifacts
• entry points / registry
• runner / artifact schema

A good third pass is to split the most implementation-heavy children into platform-specific or integration-specific issues. In practice, these often become:

• Windows no-SSH implementation slices: native PowerShell collector, Git Bash launcher/path bridge, local drop-path handling
• reporting/publication slices: fixture corpus, history index/latest manifest, publication bundle assembler, renderer, navigation tests
• registry integration slices: audit-ingestion path, machine/path alias schema, shared resolver library, coherence validator

Pick only the best 1-3 proposals per lane per round. Do not create every possible issue the subagent suggests. Prefer an issue tree that is deep enough to be executable, but still legible.

Issue-writing template

Each child issue should usually contain:

• Summary
• Why
• Scope
• Deliverables
• Parent / related issues

Keep titles specific and implementation-ready. Prefer one concrete artifact or contract per issue.

Good child issue shapes

• define schema
• create registry
• add runner
• add validator
• emit evidence bundles
• create entry-point page
• add fixture coverage
• define artifact layout

Avoid

• duplicate issues that restate the umbrella issue
• vague issues like "improve intelligence accessibility"
• mixing multiple implementation layers in one issue
• relying on subagent-written files

Verification checklist

• gh issue view <id> works for each created issue
• initiative doc links all new issues
• umbrella issue has a summary comment with the latest split
• issue titles are non-overlapping and map cleanly to parents

Minimal execution recipe

1. Read umbrella issue and existing related docs.
2. Create or update a single initiative doc in docs/.
3. Run 3 Claude subagents with lane-specific prompts.
4. Pick the best 1-3 proposals per lane.
5. Write issue bodies to /tmp/*.md.
6. Use parallel tool calls where safe:
   • write multiple /tmp issue bodies in parallel
   • create multiple gh issue create calls in parallel
7. Patch the initiative doc with the expanded issue map.
8. Comment on the umbrella issue with the new issue numbers after each wave.
9. Verify with gh issue view and read_file.
10. If continuing deeper, recurse from the strongest new children rather than reopening the whole umbrella scope.

For an approval-stage issue blocked by adversarial review, adapt the recipe as:

1. Run the 3 specialized lanes in parallel: plan-tightening, child-issue design, parent-body rewrite.
2. Apply the strongest non-conflicting revisions to the canonical plan and the parent issue body.
3. Write all selected child-issue bodies first.
4. Create the child issues in parallel with gh issue create.
5. Update the parent issue body and the plan's follow-up issue section with the new child issue numbers.
6. Post one parent summary comment that records the decomposition, dependency order, and the fact that re-review is still required if the plan changed materially.
7. Do not move the parent into approval until the revised plan has been adversarially re-reviewed.

Example outputs this pattern tends to produce

• umbrella recurring review
• framework layer: checklist, matrix, map, automation, artifact model
• operational refinement layer: routing, heartbeat, query packs, scorecard
• implementation child layer: schema, entry page, evidence bundle, runner, validator